论文标题
探索论证结构在在线辩论说服中的作用
Exploring the Role of Argument Structure in Online Debate Persuasion
论文作者
论文摘要
在线辩论论坛为用户提供了一个平台，以表达他们对有争议的主题的看法，同时遭到各种观点集的意见。自然语言处理（NLP）的现有工作表明，从辩论文本中提取的语言特征和编码听众特征的特征在说服研究中都是至关重要的。在本文中，我们旨在进一步调查在线辩论中说服力中论证的话语结构的作用。特别是，我们使用因子图模型从在线辩论平台中获取辩论的参数结构的功能，并将这些功能纳入基于LSTM的模型，以预测使最令人信服的参数的辩论者。我们发现，合并参数结构功能在在在线辩论中评估论点的说服力方面取得更好的预测性能中起着至关重要的作用。
Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the better predictive performance in assessing the persuasiveness of the arguments in online debates.
